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Human Motion Capturing and Activity Recognition Using Wearable Sensor Networks

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Developing Support Technologies

Part of the book series: Biosystems & Biorobotics ((BIOSYSROB,volume 23))

Abstract

Wearable sensor networks enable human motion capture and activity recognition in-field. This technology found widespread use in many areas, where location independent information gathering is useful, e.g., in healthcare and sports, workflow analysis, human-computer-interaction, robotics, and entertainment. Two major approaches for deriving information from wearable sensor networks are in focus here: the model-based estimation of 3D joint kinematics based on networks of inertial measurement units (IMUs) and the activity recognition based on multimodal body sensor networks using machine learning algorithms. The characteristics, working principles, challenges, potentials, and target applications of these two approaches are described individually and in synergy.

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Acknowledgements

This article is a joint work of the Interdisciplinary Junior Research Group wearHEALTH at TUK, funded by the BMBF (16SV7115), and the Embedded Intelligence Department at DFKI. Gabriele Bleser and Bertram Taetz (wearHEALTH) focused on IMU based 3D kinematics estimation, while Paul Lukowicz (Embedded Intelligence) focused on human activity recognition based on multimodal body sensor networks.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Kaiserslautern, Gottlieb-Daimler-Str. 48, 67663, Kaiserslautern, Germany

    Gabriele Bleser & Bertram Taetz

  2. German Research Center for Artificial Intelligence, Trippstadter Str. 122, 67663, Kaiserslautern, Germany

    Paul Lukowicz

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  1. Gabriele Bleser
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  2. Bertram Taetz
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  3. Paul Lukowicz
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Correspondence to Gabriele Bleser .

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Editors and Affiliations

  1. Laboratory of Manufacturing Technology, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, Hamburg, Germany

    Athanasios Karafillidis

  2. Laboratory of Manufacturing Technology, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, Hamburg, Germany

    Robert Weidner

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Bleser, G., Taetz, B., Lukowicz, P. (2018). Human Motion Capturing and Activity Recognition Using Wearable Sensor Networks. In: Karafillidis, A., Weidner, R. (eds) Developing Support Technologies. Biosystems & Biorobotics, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-01836-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-01836-8_19

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